Content uploaded by David J. Nowak
Author content
All content in this area was uploaded by David J. Nowak on Jan 28, 2019
Content may be subject to copyright.
Modeled PM
2.5
removal by trees in ten U.S. cities and associated health
effects
David J. Nowak
a
,
*
, Satoshi Hirabayashi
b
, Allison Bodine
b
, Robert Hoehn
a
a
USDA Forest Service, 5 Moon Library, SUNY-ESF, Syracuse, NY 13210, USA
b
Davey Institute, 5 Moon Library, SUNY-ESF, Syracuse, NY 13210, USA
article info
Article history:
Received 5 November 2012
Received in revised form
23 March 2013
Accepted 25 March 2013
Keywords:
Urban forests
Air pollution removal
Particulate matter
Mortality
Human health
abstract
Urban particulate air pollution is a serious health issue. Trees within cities can remove fine particles from
the atmosphere and consequently improve air quality and human health. Tree effects on PM
2.5
con-
centrations and human health are modeled for 10 U.S. cities. The total amount of PM
2.5
removed annually
by trees varied from 4.7 tonnes in Syracuse to 64.5 tonnes in Atlanta, with annual values varying from
$1.1 million in Syracuse to $60.1 million in New York City. Most of these values were from the effects of
reducing human mortality. Mortality reductions were typically around 1 person yr
1
per city, but were as
high as 7.6 people yr
1
in New York City. Average annual percent air quality improvement ranged be-
tween 0.05% in San Francisco and 0.24% in Atlanta. Understanding the impact of urban trees on air quality
can lead to improved urban forest management strategies to sustain human health in cities.
Published by Elsevier Ltd.
1. Introduction
Fine particulate matter less than 2.5 microns (PM
2.5
) is associ-
ated with significant health effects that include premature mor-
tality, pulmonary inflammation, accelerated atherosclerosis, and
altered cardiac functions (e.g., Pope et al., 2004). A 10
m
gm
3
in-
crease in fine particulate matter has been associated with an
approximately 4%, 6%, and 8% increased risk in all-cause, cardio-
pulmonary and lung cancer mortality, respectively (Pope et al.,
2002). The regulation of these pollutants by the U.S. Environ-
mental Protection Agency (U.S. EPA) has resulted in significant
improvements in air quality over the last decade with reductions in
monitored PM
2.5
from 2000 to 2007 associated with 22 000e
60 000 net avoided premature mortalities in the United States
(Fann and Risley, 2011).
Trees are often a major element of the city landscape with
urban tree cover in the United States averaging 35.0% (Nowak and
Greenfield, 2012a). Trees directly affect particulate matter in the
atmosphere by removing particles (e.g., Beckett et al., 2000a;Freer-
Smith et al., 2004) and emitting particles (e.g., pollen) or through
resuspension of particles captured on the plant surface. Some
captured particles can be absorbed into the tree, though most
particles that are intercepted are retained on the plant surface. The
intercepted particle often is resuspended to the atmosphere,
washed off by rain, or dropped to the ground with leaf and twig
fall. Consequently, vegetation is only a temporary retention site
for many atmospheric particles. Trees can also affect particulate
matter concentration by altering air temperatures, emitting volatile
organic compounds and altering energy use (e.g., tree shade on
building, altering wind speeds, cooling air temperatures) and
consequent emissions from power plants (e.g., Heisler, 1986;Smith,
1990;Beckett et al., 1998). At the local scale, interior parts of forest
patches within urban areas can have substantially lower concen-
trations of particulate matter than forest edges (Cavanagh et al.,
2009).
To date, most research related to urban trees and particulate
matter has focused on removal of particulate matter less than 10
microns (PM
10
) by trees. Increasing total tree cover in West Mid-
lands, UK from 3.7% to 16.5% is estimated to reduce average primary
PM
10
concentrations by 10% from 2.3 to 2.1
m
gm
3
(removing 110
tonnes per year); increasing tree cover from 3.6% to 8% in Glasgow,
UK is estimated to reduce PM
10
concentrations by 2%, (removing 4
tonnes per year) (McDonald et al., 2007). In the Greater London
area (UK), urban tree canopies are estimated to remove between
852 and 2121 tonnes of PM
10
annually, which equates to 0.7%e1.4%
PM
10
air quality improvement (Tallis et al., 2011). A 10 10 km grid
in London with 25% tree cover was estimated to remove 90.4 tonnes
of PM
10
per year, which equated to the avoidance of 2 deaths and 2
*Corresponding author.
E-mail address: dnowak@fs.fed.us (D.J. Nowak).
Contents lists available at SciVerse ScienceDirect
Environmental Pollution
journal homepage: www.elsevier.com/locate/envpol
0269-7491/$ esee front matter Published by Elsevier Ltd.
http://dx.doi.org/10.1016/j.envpol.2013.03.050
Environmental Pollution 178 (2013) 395e402
hospital emissions per year (Tiwary et al., 2009). PM
10
removal by
urban trees in the United States has been estimated at 214 900
tonnes per year (Nowak et al., 2006a).
Various studies to date have investigated the removal rate and
resuspension of PM
2.5
by trees (e.g., Beckett et al., 2000b;Freer-
Smith et al., 2004,2005;Pullman, 2009), but none have esti-
mated the overall impact of the trees and forests in a city on PM
2.5
concentrations. The objective of this paper is to estimate, on an
hourly basis over the course of a year, the amount of PM
2.5
removal
and resuspension by trees within 10 U.S. cities and its effect on
PM
2.5
concentrations, including the associated values and impact
on human health.
2. Methods
To estimate the effects and associated values of PM
2.5
removal by urban trees in
10 cities (Table 1), four types of analyses were conducted that estimated: 1) the total
leaf area in the city on a daily basis, 2) the hourly flux and resuspension of PM
2.5
to
and from the leaves, 3) the effects of hourly PM
2.5
removal by trees on PM
2.5
con-
centration in the atmosphere, and 4) the health incidence impacts and monetary
value of the change in PM
2.5
concentration using information from the U.S. EPA
Environmental Benefits Mapping and Analysis Program (BenMAP) model (U.S. EPA,
2012a).
2.1. City tree population parameters
To determine the leaf surface area within the 10 U.S. cities, field data on trees
were measured within randomly selected 0.04 ha plots and analyzed using the i-
Tree Eco model (Table 1;Nowak et al., 2008). The model estimated the total leaf
area index per unit of tree cover (LAI ¼one-sided leaf area in crown divided by
projected crown area on the ground; i.e., the number of layers of leaves within the
crown) and percent of the tree population that is evergreen. Tree cover within each
city was estimated by photo interpreting random points throughout each city with
recent imagery (Table 1;Nowak and Greenfield, 2012b). Total city leaf area was
estimated by multiplying city tree cover (m
2
) by city LAI per unit of tree cover
(m
2
m
2
). Leaf area index values were combined with percent evergreen infor-
mation and local leaf on and leaf off dates to estimate total daily leaf surface area in
each city.
2.2. PM
2.5
removal by trees
Hourly pollution removal or flux (Fin
m
gm
2
hr
1
) can be estimated as:
F¼V
d
C(1)
where V
d
is the deposition velocity of the pollutant to the leaf surface (m h
1
) and C
is pollutant concentration (
m
gm
3
) (e.g., Hicks et al., 1989). Daily (24-h) pollution
concentrations of PM
2.5
in each city were obtained from U.S. EPA monitors for the
year of 2010. If more than one monitor existed, the daily values were averaged for
each day to produce a city average value. The average daily value was used to
represent the hourly concentration values throughout the day.
Deposition velocities of PM
2.5
to trees were estimated from the literature and
varied with wind speed (Beckett et al., 2000b;Freer-Smith et al., 2004;Pullman,
2009). These papers measured deposition velocities to tree leaves from 17 tree
species under wind speeds of 1, 3, 6, 8, 9 and 10 m s
1
. For each wind speed, the
median deposition velocities from the measured deposition velocities was used to
estimate the V
d
for that wind speed per unit leaf area (Table 2). The standard error of
the estimates among the species was used to estimate a potential range of values of
deposition velocities. The 95 percent confidence interval of median deposition ve-
locity per wind speed was used to estimate a maximum deposition for the wind
speed. As 95 percent confidence interval for the lower range of deposition velocities
produced negative deposition velocities, the minimum average V
d
from any species
was used to represent the minimum V
d
for the wind speed. To estimate the V
d
for
wind speeds between 1 and 10 m s
1
that did not have a measured V
d
, values were
interpolated between the closest measured values. For wind speeds above 10 m s
1
,
the V
d
for 10 m s
1
was used; for a wind speed of 0 m s
1
, the V
d
was assumed to be
0ms
1
(Table 3).
Resuspension of PM
2.5
from trees was estimated from Pullman (2009) and
varied with wind speed. This paper measured percent resuspension of PM
2.5
from
tree leaves of three tree species under wind speeds of 6.5, 10 and 13 m s
1
. The
average percent resuspension for the trees species and wind speed was calculated
(Table 3). As the percent resuspension for the wind speed of 6.5 m s
1
was 9.5%, a
value of 9% was assumed for a wind speed of 6 m s
1
and 10% for 7 m s
1
. The percent
resuspension for a wind speed of 0 m s
1
was assumed to be 0%. To estimate the
percent resuspension for wind speeds between 0 and 13 m s
1
that did not have
measured resuspension rates, values were interpolated from the closest measured
values (or assumed value at wind speed of 0 m s
1
). For wind speeds above 13 m s
1
,
the percent resuspension rate for 13 m s
1
was used (Table 3).
To calculate pollution removal, local city weather data from the National Cli-
matic Data Center were used to obtain hourly wind speed and precipitation data.
Hourly flux values to trees in the city (Eq. (1);
m
gm
2
h
1
) were multiplied by total
leaf surface area (m
2
) with hourly V
d
based on local wind speed (Table 3). Flux values
were accumulated hourly with a percent of the total accumulated PM
2.5
over the
current and previous hours resuspended back to the atmosphere hourly based on
local wind speed. PM
2.5
was accumulated upon leaves and resuspended from leaves
Table 1
Number of field plots and tree cover estimates in cities.
City Plots Tree
cover (%)
b
Cover
year
c
# #km
2
Year
a
Atlanta, GA
d
205 0.6 1997 52.1 2009
Baltimore, MD
d
195 0.9 2009 28.5 2005
Boston, MA
d
217 1.5 1996 27.9 2008
Chicago, IL
e
745 1.2 2007 18.0 2009
Los Angeles, CA
f
348 0.3 2007e2008 20.6 2009
Minneapolis, MN
g
110 0.7 2004 34.1 2008
New York, NY
h
206 0.3 1996 19.7 2009
Philadelphia, PA
i
210 0.6 1996 20.8 2010
San Francisco, CA
j
194 1.6 2004 16.0 2011
Syracuse, NY
d
198 3.0 2009 26.9 2009
#Number of plots.
a
Year of plot field data collection.
b
Cover estimates from photo-interpretation (Nowak and Greenfield, 2012b).
Philadelphia and San Francisco are unpublished estimates, U.S. Forest Service, Syr-
acuse, NY.
c
Year of imagery for cover estimates.
d
Unpublished data from U.S. Forest Service, Syracuse, NY.
e
Nowak et al., 2010.
f
Nowak et al., 2011.
g
Nowak et al., 2006b.
h
Nowak et al., 2007a.
i
Nowak et al., 2007b.
j
Nowak et al., 2007c.
Table 2
Summary of average deposition velocities (cm s
1
)ofPM
2.5
by wind speed from the
literature per unit leaf area.
Species Wind speed (m s
1
)
1 3 6 8.5
a
10
Quercus petraea
b
0.831 1.757 3.134
Alnus glutinosa
b
0.125 0.173 0.798
Fraxinus excelsior
b
0.178 0.383 0.725
Acer pseudoplatanus
b
0.042 0.197 0.344
Pseudotsuga menziesii
b
1.269 1.604 6.04
Eucalyptus globulus
b
0.018 0.029 0.082
Ficus nitida
b
0.041 0.098 0.234
Pinus nigra
c
0.13 1.15 19.24 28.05
Cupressocyparis x leylandii
c
0.08 0.76 8.24 12.2
Acer campestre
c
0.03 0.08 0.46 0.57
Sorbus intermedia
c
0.04 0.39 1.82 2.11
Populus deltoides
c
0.03 0.12 1.05 1.18
Pinus strobus
d
0.0108
Tsuga canadensis
d
0.0193
Tsuga japonica
d
0.0058
Picea abies
e
0.0189
Picea abies
e
0.038
Median 0.030 0.152 0.197 0.924 2.110
SE
f
0.012 0.133 0.281 1.610 5.257
Maximum
g
0.057 0.442 0.862 5.063 14.542
Minimum
h
0.006 0.018 0.029 0.082 0.570
a
Combination of 8 and 9 m s
1
wind speeds.
b
From Freer-Smith et al. (2004).
c
From Beckett et al. (2000b).
d
From Pullman (2009). Included particles up to 3.0
m
m in diameter.
e
From Pullman (2009). Based on maximum and minimum of reported range.
Included particles up to 3.8
m
m in diameter.
f
Standard error.
g
Based on 95 percent confidence interval above median value.
h
Based on lowest recorded value for any species.
D.J. Nowak et al. / Environmental Pollution 178 (2013) 395e402396
during non-precipitation periods. During precipitation events, the accumulated
PM
2.5
was assumed to be washed off to the ground surface depending upon the
magnitude of the precipitation event (Pe in mm). As leaves capture about 0.2 mm of
precipitation (Wang et al., 2008) before runoff from the leaf, the total precipitation
storage capacity (Ps in mm) of the canopy was calculated as 0.2 LAI. If Pe was
greater than Ps, then all particles were assumed to be removed from the leaves and
resuspension dropped to zero. When the Pe was less than Ps, no particles were
removed from the leaves as there was no runoff from the leaves. After the rain
stopped, PM
2.5
began accumulating on and resuspending from leaves again. Water
on the leaves after rain events was reduced hourly based on evaporation rates
calculated from meteorological conditions. The annual flux to tree leaves was esti-
mated as the total PM
2.5
washed off leaves during the year plus the amount
remaining on leaves at the end of the year.
2.3. Change in PM
2.5
concentration
To estimate percent air qualityimprovement due to dry deposition (Nowak et al.,
2000), hourly boundary layer heights were used in conjunction with local hourly
fluxes and resuspension rates in each city. Daily morning and afternoon mixing
heights were calculated using the EPA’s mixing height program (U.S. EPA,1981) with
upper air data from the nearest radiosonde station. These mixing heights were then
interpolated to produce hourly boundary layer height values using the EPA’s
PCRAMMIT program (U.S. EPA, 1995). Minimum boundary-layer heights were set to
150 m during the night and 250 m during the day based on estimated minimum
boundary-layer heights in cities. Hourly mixing heights (m) were used in conjunc-
tion with pollution concentrations (
m
gm
3
) to calculate the amount of pollution
within the mixing layer (
m
gm
2
). This extrapolation from ground-layer concen-
tration to total pollution within the boundary layer assumes a well-mixed boundary
layer, which is common in the daytime (unstable conditions) (Colbeck and Harrison,
1985). Hourly change in PM
2.5
concentration was calculated as:
D
C¼
D
P
t
=ðBL CAÞ(2)
where
D
C¼change in PM
2.5
concentration (
m
gm
3
),
D
P
t
¼change in PM
2.5
mass
(
m
g) due to the net of effect of PM
2.5
removal (flux) and resuspension from leaves,
BL ¼boundary layer height (m) and CA ¼city area (m
2
). Percent air quality
improvement was calculated as:
%
D
¼
D
P
t
=ð
D
P
t
þP
a
Þ(3)
where P
a
¼PM
2.5
mass in the atmosphere (
m
g), which equals measured concen-
tration (
m
gm
3
)BL CA.
2.4. Health incidence effects and monetary value of PM
2.5
removal
For the 10 U.S. cities, the U.S. EPA’s BenMAP program was used to estimate the
incidence of adverse health effects (i.e., mortality and morbidity) and associated
monetary value that result from changes in PM
2.5
concentrations. BenMAP is a
Windows-based computer program that uses Geographic Information System (GIS)
based data to estimate the health impacts and economic value when populations
experience changes in air quality (U.S. EPA, 2012a). The model uses air quality grids
to determine the change in pollution concentration, concentration-response func-
tions to estimate the change in adverse health effects, and valuation functions to
calculate the associated economic value (Table 4). BenMAP was used to obtain
incidence and value results for each county within which the 10 cities reside.
The air quality grids used for this analysis were for baseline (2000) and control
(2006) years that had the greatest change in pollution concentration based on
national pollution trends (http://www.epa.gov/airtrends/index.html). The pollution
concentration for the grids was interpolated from existing pollution data sets from
EPA pollutant monitors using Voronoi neighborhood averaging.
Several functions were used to estimate incidence and value for the following
common health effects of PM
2.5
: acute bronchitis, acute myocardial infarction, acute
respiratory symptoms, asthma exacerbation, chronic bronchitis, emergency room
visits, hospital admissions ecardiovascular or respiratory, lower respiratory symp-
toms, mortality, upper respiratory symptoms, and work loss days. The concentration-
response functions that were used for the PM
2.5
analysis (Table 4) haveseveral inputs
including air quality metrics (e.g., 24-h mean) and age of the population (e.g., 18e64
years old, 65e99 years old).
The model was run using population statistics from the U.S. Census 2010 county
dataset using an economic forecasting model described in the BenMAP user manual
(Abt Associates, 2010). BenMAP configures Census block populations into grid cell
level data and the calculation is at grid cell level. BenMap data were then aggregated
to the county level. The health effects categories potentially had multiple estimates
corresponding to different air quality metrics and age groups. Different age groups
were represented because the concentration-response functions are age specific and
incidence rate can vary across different age groups. Multiple estimates were pooled
by either averaging the estimates using the random/fixed effects method or sum-
ming the estimates depending on which process was appropriate. In the end, a final
estimate was produced to cover all possible metrics and age groups within a health
category. For example, equations for 0e17, 18e64, and 65e99 age groups were
summed to produce an estimate for 0e99 age group. More details on the BenMAP
model are found in the literature (Davidson et al., 2007;Abt Associates, 2010;U.S.
EPA, 2012a).
To estimate each individual health category incidence and dollar value effect at
the city scale, the county estimates were divided by the county population by age
group and change in pollution concentration to produce an estimate of number of
incidences or dollar value per person per age group per change in
m
gm
3
, similar to
the procedure used in U.S. EPA (2012b). This value was then multiplied by the city
population per age group and change in PM
2.5
concentration due to trees in the city
to estimate the tree effects on incidence and value for each health category. The
dollar values for all health categories were summed to determine the total value of
PM
2.5
effects from trees in each city.
3. Results
Total amount of PM
2.5
removal annually by trees varied from 4.7
tonnes in Syracuse to 64.5 tonnes in Atlanta, with values varying
from $1.1 million in Syracuse to $60.1 million in New York City
(Table 5). Most of these values were dominated by the effects of
reducing human mortality (Table 6). The average value per mor-
tality incidence was $7.8 million. Mortality reductions were typi-
cally around 1 person yr
1
per city, but were as high as 7.6 people
yr
1
in New York City. The net removal amounts per square meter
of canopy cover varied from 0.13 g m
2
yr
1
in Los Angeles to
0.36 g m
2
yr
1
in Atlanta. The average annual percent air quality
improvement ranged between 0.05% in San Francisco and 0.24% in
Atlanta (Table 5).
The average health benefits value per hectare of tree cover was
about $1 600, but varied from $500 in Atlanta and Minneapolis to
$3800 in New York (Table 5). The value per tonne of PM
2.5
averaged
$682 000, but varied from $142 000 in Atlanta to $1 610 000 in New
York. The health benefits value per reduction of 1
m
gm
3
also
varied from $122 million in Syracuse to $6.2 billion in New York,
with an overall average of $1.6 billion.
The interactions among variable V
d
, resuspension, and precipi-
tation can be seen in an hourly graph of total accumulation by tree
canopies, in which removal of PM
2.5
by trees occurs during precip-
itation events when particles on leaves are washed off and trans-
ferred to the soil. Total accumulation stabilizes around 3500
m
gm
2
of tree cover among the cities with variations up (net removal) and
down (net resuspension) hourly (Fig. 1). Average hourly cumulative
flux in the cities ranged between 2100
m
gm
2
of tree cover in
Philadelphia to 5700
m
gm
2
of tree cover in San Francisco. Average
reduction in PM
2.5
concentrations ranged between 0.006
m
gm
3
in
Philadelphia and San Francisco to 0.03
m
gm
3
in Atlanta (Table 5). Of
all the particles intercepted by leaves, on average 34.0 percent were
resuspended, with percent resuspension varying from 26.7 percent
in Syracuse to 42.6 percent in San Francisco.
Table 3
Deposition velocities and percent resuspension by wind speed per unit leaf area.
Wind speed
(m s
1
)
Deposition velocity (V
d
) Resuspension (%)
Average
(cm s
1
)
Minimum
(cm s
1
)
Maximum
(cm s
1
)
0 0.00 0.000 0.000 0
1 0.03 0.006 0.042 1.5
2 0.09 0.012 0.163 3
3 0.15 0.018 0.285 4.5
4 0.17 0.022 0.349 6
5 0.19 0.025 0.414 7.5
6 0.20 0.029 0.478 9
7 0.56 0.056 1.506 10
8 0.92 0.082 2.534 11
9 0.92 0.082 2.534 12
10 2.11 0.570 7.367 13
11 2.11 0.570 7.367 16
12 2.11 0.570 7.367 20
13 2.11 0.570 7.367 23
D.J. Nowak et al. / Environmental Pollution 178 (2013) 395e402 397
4. Discussion
The removal of PM
2.5
by urban trees is substantially lower than
for PM
10
(Nowak et al., 2006a), but the health implications and
values are much higher. The value of PM
2.5
removal in the cities
ranged from $1.1 million yr
1
(Syracuse) to 60.1 million yr
1
(New
York). The annual values per tonne removed ranged between
$142 000 (Atlanta) and $1.6 million (New York). These values are
substantially higher than value estimates for PM
10
($4500 t
1
),
which are based on median national externality values (Murray
et al., 1994). Most of this PM
2.5
removal value is derived from the
reduction in human mortality due to reduced PM
2.5
concentrations.
Reduction in human mortality ranged from 1 person per 365 000
people in Atlanta to 1 person per 1.35 million people in San Fran-
cisco (average ¼1 person per 990 000 people). Overall, the greatest
effect of trees on reducing health impacts of PM
2.5
occurred in New
York due to its relatively large human population and moderately
high removal rate and reduction in concentration. The greatest
Table 5
Estimated removal of PM
2.5
by trees and associated value in several U.S. cities.
City Total (t yr
1
) Range (t yr
1
) Value ($ yr
1
) Effect
a
:m
2
yr
1
D
C
b
(
m
gm
3
)AQ
c
(%)
(g) ($)
Atlanta, GA 64.5 (8.5e140.4) 9 170 000 0.36 0.05 0.030 0.24
Baltimore, MD 14.0 (1.8e29.5) 7 780 000 0.24 0.13 0.010 0.09
Boston, MA 12.7 (2.0e35.6) 9 360 000 0.32 0.23 0.020 0.19
Chicago, IL 27.7 (4.0e68.1) 25 860 000 0.26 0.24 0.011 0.09
Los Angeles, CA 32.2 (4.2e70.3) 23 650 000 0.13 0.09 0.009 0.07
Minneapolis, MN 12.0 (1.6e28.2) 2 610 000 0.23 0.05 0.010 0.08
New York, NY 37.4 (5.1e97.2) 60 130 000 0.24 0.38 0.010 0.09
Philadelphia, PA 12.3 (1.6e28.1) 9 880 000 0.17 0.14 0.006 0.08
San Francisco, CA 5.5 (0.8e14.4) 4 720 000 0.29 0.25 0.006 0.05
Syracuse, NY 4.7 (0.6e10.8) 1 100 000 0.27 0.06 0.009 0.10
a
Average effects per square meter of tree cover per year: removal in grams and dollar value.
b
Average annual reduction in hourly concentration.
c
Average percent air quality improvement.
Table 4
Concentration-response functions used for PM
2.5
analyses. Daily 24-h mean concentrations data were aggregated by seasonal metric. Valuation procedure for health effects are
also noted.
Health effect Concentration response function
reference
Seasonal metric Start age End age
Acute Bronchitis
a
Dockery et al., 1996 Quarterly 8 12
Acute myocardial infarction
b
Acute myocardial infarction, nonfatal Peters et al., 2001 Annual 18 99
Pope et al., 2006 Annual 0 99
Sullivan et al., 2005 Annual 0 99
Zanobetti and Schwartz, 2006 Annual 0 99
Zanobetti et al., 2009 Annual 0 99
Acute respiratory symptoms
a
Minor restricted activity days Ostro and Rothschild, 1989 Annual 18 64
Asthma exacerbation
a
Asthma exacerbation, cough Mar et al., 2004 Annual 6 18
Asthma exacerbation, shortness of breath Mar et al., 2004 Annual 6 18
Asthma exacerbation, wheeze Ostro et al., 2001 Annual 6 18
Chronic bronchitis
a,b
Abbey et al., 1995 Quarterly 27 99
Emergency room visits, respiratory
b
Emergency room visits, asthma Mar et al., 2010 Annual 0 99
Norris et al., 1999 Annual 0 17
Slaughter et al., 2005 Annual 0 99
Hospital admissions, cardiovascular
b
HA, all cardiovascular (less myocardial
infarctions)
Bell et al., 2008 Annual 65 99
Moolgavkar, 2000 Annual 18 64
Moolgavkar, 2003 Annual 65 99
Peng et al., 2008 Annual 65 99
Peng et al., 2009 Annual 65 99
Zanobetti et al., 2009 Annual 65 99
Hospital admissions, respiratory
b
HA, all respiratory Zanobetti et al., 2009 Annual 65 99
Lower respiratory symptoms
a
Schwartz and Neas, 2000 Annual 7 14
Mortality
c
Mortality, all cause Laden et al., 2006 Quarterly 25 99
Woodruff et al., 1997 Quarterly 0 1
Woodruff et al., 2006 Quarterly 0 1
Upper respiratory symptoms
a
Pope et al., 1991 Quarterly 9 11
Work loss days
d
Ostro, 1987 Annual 18 64
Valuation procedure (U.S. EPA, 2012a).
a
Willingness to pay.
b
Cost of illness.
c
Value of statistical life.
d
Median daily wage.
D.J. Nowak et al. / Environmental Pollution 178 (2013) 395e402398
overall removal by trees was in Atlanta due to its relatively high
percent tree cover and PM
2.5
concentrations (12.6
m
gm
3
).
The net removal rates per square meter of tree cover varied
among cities between 0.36 g m
2
yr
1
(Atlanta) and 0.13 g m
2
yr
1
(Los Angeles), with Los Angeles having the highest PM
2.5
concen-
trations (13.8
m
gm
3
), but the lowest amount (392 mm yr
1
) and
frequency of rainfall (247 h yr
1
). The average amount and fre-
quency of precipitation among the cities were 644 mm yr
1
and
394 h yr
1
respectively. On average, about 24 g m
2
yr
1
of PM
2.5
removal equated to 1
m
gm
3
reduction in PM
2.5
concentrations, but
results varied from 12 g m
2
yr
1
in Atlanta to 45 g m
2
yr
1
in San
Francisco.
Removal rates per unit canopy and effects on local PM
2.5
con-
centration varies among cities based on amount of tree cover e
increased cover increases removal, pollution concentration e
increased concentration increases removal, length of growing
season and percent evergreen leaf area elonger growing season
increases removal by deciduous species, and meteorological con-
ditions. The meteorological conditions (precipitation, wind speed
and boundary layer heights) interact to affect PM
2.5
removal and
concentrations. Increased precipitation tends to increase tree
removal via the washing of particles from the leaf surfaces. The low
removal rate in Los Angeles is likely due, in part, to limited pre-
cipitation. Wind speeds affect resuspension and boundary layer
heights. Greater resuspension reduces the overall removal rate by
trees; increased boundary layer heights reduce the overall percent
impact of trees on pollutant concentrations, but also reduce PM
2.5
concentrations. Maximum percent air quality improvements ten-
ded to occur under windy conditions (increased V
d
) with low
boundary layer heights (increased impact of removal on pollutant
concentration) and relatively clean leaves (low amount of particles
to be resuspended).
When resuspension is greater than the removal rate, trees can
increase local concentrations due to previously deposited particles
reentering the atmosphere (Fig. 1). Although PM
2.5
removal by trees
in the analyzed cities lead to reduced overall particulate concen-
trations, it is possible that even though trees remove particulate
matter, they could increase overall particulate concentrations. This
overall increase in concentrations could occur depending upon the
meteorological conditions when particles are deposited and
resuspended. If particulate removal occurs under high boundary
layer conditions, but resuspension occurs mostly under low
boundary layer conditions, the amount of removal would cause a
lower reduction in concentrations than the increased concentration
effect due to resuspension. Thus timing of removal relative to
boundary layer heights has a substantial impact on overall con-
centration changes. Overall impacts and dollar values also varied
based on population density and composition, along with the tree
effects on concentration.
Though there are various limitations to these estimates, the
results indicate a first-order approximation of the magnitude of
tree effects on PM
2.5
concentrations. Limitations of the analysis
include: a) assumption that all particles are removed from leaves by
precipitation events that cover the entire leaf area as some particles
may remain on leaves or some particles may be removed in light
rain events (Pe <Ps), b) there is no assumed interaction with water
on leaves after precipitation events, c) some precipitation events
may be in the form of snow, which may limit removal; however
these events are relatively infrequent and limited to only evergreen
trees removal that accounts for only about 18% of the total leaf area
among the cities, d) measured deposition velocities used to calcu-
late the average V
d
are based on varying particle sizes with some
particles greater than 2.5
m
m (up to 3.8
m
m) and particle size affects
the deposition velocity (e.g., Gallagher et al., 1997)eit is assumed
the measured deposition velocities represent the average for the
particle distribution in the atmosphere, e) wind speeds and
therefore V
d
and resuspension can vary locally, though an average
wind speed is used to represent the entire city, f) tree volatile
Table 6
Reduction in number of incidences and associated dollar value for various health effects due to PM
2.5
reduction from trees.
Health effect
a
No. Value No. Value No. Value No. Value No. Value
Atlanta, GA Baltimore, MD Boston, MA Chicago, IL Los Angeles, CA
Acute bronchitis 0.6 60 0.4 30 0.5 50 1.8 160 2.1 180
Acute myocardial infarction 0.3 26 300 0.2 14 600 0.3 28 400 0.9 78 800 0.6 49 300
Acute respiratory symptoms 488.7 47 900 240.9 23 600 502.5 49 200 1125.2 110 300 1263.6 123 900
Asthma exacerbation 243.8 19 800 138.3 11 200 243.0 19 800 770.0 62 600 936.4 76 100
Chronic bronchitis 0.4 104 000 0.2 53 000 0.3 96 000 0.9 247 000 1.0 285 000
Emergency room visits 0.4 180 0.9 390 0.4 190 1.2 510 1.1 470
Hospital admissions, cardiovascular 0.2 7700 0.2 6200 0.2 6300 0.5 17 400 0.3 12 700
Hospital admissions, respiratory 0.1 4400 0.1 2300 0.1 4600 0.4 13 800 0.3 9000
Lower respiratory symptoms 7.2 400 4.4 200 6.5 300 22.9 1200 25.5 1300
Mortality 1.2 8 940 000 1.0 7 670 000 1.2 9 140 000 3.2 25 300 000 3.0 23 000 000
Upper respiratory symptoms 6.4 300 3.7 200 5.2 200 18.3 800 21.0 900
Work loss days 84.8 16 300 40.8 6000 87.5 15 300 192.1 35 000 217.4 37 000
Total na 9 170 000 na 7 780 000 na 9 360 000 na 25 900 000 na 23 600 000
Minneapolis, MN New York, NY Philadelphia, PA San Francisco, CA Syracuse, NY
Acute bronchitis 0.2 20 4.5 400 0.5 50 0.2 20 0.1 10
Acute myocardial infarction 0.1 5800 1.4 129 300 0.2 22 400 0.1 7400 0.0 2400
Acute respiratory symptoms 146.8 14 400 2930.9 287 300 313.8 30 800 207.3 20 300 49.5 4900
Asthma exacerbation 80.9 6600 1919.3 156 000 205.8 16 700 77.2 6300 37.7 3100
Chronic bronchitis 0.1 29 400 2.4 682 000 0.3 71 500 0.2 51 600 0.0 9600
Emergency room visits 0.1 40 8.0 3300 0.4 160 0.1 30 0.0 20
Hospital admissions, cardiovascular 0.0 1200 1.2 46 200 0.1 5400 0.1 2000 0.0 500
Hospital admissions, respiratory 0.0 600 0.7 22 700 0.1 3000 0.0 1500 0.0 300
Lower respiratory symptoms 2.2 100 55.7 2900 6.1 300 2.0 100 1.0 50
Mortality 0.3 2 550 000 7.6 58 700 000 1.2 9 720 000 0.6 4 620 000 0.1 1 080 000
Upper respiratory symptoms 1.9 100 45.0 2000 5.1 200 1.7 100 0.8 40
Work loss days 25.0 4800 504.0 92 100 53.7 8500 36.0 7900 8.3 1400
Total na 2 610 000 na 60 100 000 na 9 880 000 na 4 720 000 na 1 100 000
a
Incidence values of 0.0 indicate a value of less than 0.05.
D.J. Nowak et al. / Environmental Pollution 178 (2013) 395e402 399
organic compound emissions and their potential contribution to
PM
2.5
concentrations are not considered (e.g., Hodan and Barnard.,
2004), g) V
d
is assumed equal for all leaves within a tree canopy;
however interior leaves are likely to have lower wind speeds and
therefore lower V
d
and resuspension rates, but most leaf surface
area is not within the interior of the tree canopy, h) rainfall in-
tensity is not considered and may affect washoff rates; i) use of 24-h
average concentration data to estimate the hourly concentrations
during the day as concentrations will vary locally (e.g., likely higher
concentrations near roadways) and temporally, and j) the boundary
layer is assumed to be well-mixed (unstable), which will likely lead
to conservative estimates of concentration reductions during stable
conditions. Future research and more detailed modeling may help
overcome these current limitations.
Despite the limitations, there are advantages to these modeling
estimates, which include: a) use of locally measured tree, weather
and pollution data to assess PM
2.5
effects, b) use of measured V
d
and
resuspension rates to estimate removal and resuspension, and c)
interaction of V
d
and resuspension with local hourly wind speeds.
The interactions and variations of PM
2.5
removal and resuspension
with wind speed (Fig. 1) illustrate how the PM
2.5
flux can vary
hourly, yielding positive and negative concentration changes
throughout a day. Average wind speed in the cities was 3.7 m s
1
with a maximum speed of 20.6 m s
1
. The average deposition ve-
locity to tree canopies was 0.65 cm s
1
, which is above the typical
range listed for particles less than 2
m
m(<0.5 cm s
1
;Lovett, 1994).
However, the average V
d
estimate for PM
2.5
(0.65 cm s
1
) does not
include resuspension, which considering a 34 percent average
resuspension rate, would lower the V
d
estimate to about 0.43 cm s
1
.
In this simulation, the movement of the particles from the tree
leaves to the soil environment occurs via precipitation. The greater
the amount of particles on a leaf just prior to a precipitation event,
the greater the overall effect of the trees on removal of PM
2.5
from
the atmosphere. Between rainfall events, the amount of particles
retained on tree canopies averages 3500
m
gm
2
, but fluctuates
through time based on wind speed. Frequent rainfall would likely
maximize tree effectiveness on removing particles from the
atmosphere and transferring them to the soil environment. How-
ever, not all particles will be resuspended or washed off with pre-
cipitation, some particle will adhere to waxy leaf surfaces and be
transferred to the soil via leaf drop and leaf decomposition (e.g.,
Joureava et al., 2002).
This citywide modeling focuses on broad-scale estimates of tree
effects on PM
2.5
. Local-scale effects likely differ depending upon
vegetation designs. At the local scale, PM
2.5
concentrations can be
increased if trees: a) trap the particles beneath tree canopies near
emission sources (e.g., along road ways, Gromke and Ruck (2009)),
b) limit dispersion by reducing wind speeds (e.g., Vos et al., 2012)
and/or c) lower boundary layer heights by reducing wind speeds
(e.g., Nowak et al., 2000). Under stable atmospheric conditions
(limited mixing), particle removal by trees could lead to increased
reductions in pollution concentrations at the ground level. In
addition, if some local sources of PM
2.5
come from wind-borne
soils, tree cover can reduce these particles by reducing wind
speeds. Large stands of trees can also reduce pollutant concentra-
tions in the interior of the stand due to increased distance from
emission sources and increased dry deposition (e.g., Dasch, 1987;
Cavanagh et al., 2009). Thus, local scale design with trees and for-
ests are important for reducing local scale PM
2.5
concentrations.
More research is needed on these local scale issues as local scale
designs with trees need to consider vegetation configuration and
sourceesink relationships to maximize tree effects on reducing
PM
2.5
concentrations and minimizing human exposure to PM
2.5
.
In addition to PM
2.5
removal, tree also remove other air pol-
lutants (e.g., ozone, sulfur dioxide, nitrogen dioxide; Nowak et al.,
2006a) and emit volatile organic compounds that can contribute
to ozone formation (e.g., Chameides et al., 1988). Managers need to
understand the magnitude of tree effects on air pollution to better
manage urban vegetation to improve air quality. To aid in assisting
urban forest planners and managers, a free model (i-Tree: www.
itreetools.org) has been developed to aid cities in quantifying
pollution removal by trees and other environmental services.
Improving air quality with vegetation in cities can lead to improved
human health and substantial health care savings.
Fig. 1. Cumulative hourly flux of PM
2.5
per square meter of tree cover in New York City starting at 1 am on July 8, 2010. Increasing flux values indicate hourly removal, decreasing
values indicate a net resuspension. Precipitation periods could remove particles from leaves and transport them to the ground. This transported amount was calculated as a net
removal from trees.
D.J. Nowak et al. / Environmental Pollution 178 (2013) 395e402400
5. Conclusions
Modeling of broad-scale effects of pollution removal by trees on
PM
2.5
concentrations and human health reveal that trees can pro-
duce substantial health improvements and values in cities. More
research is needed to improve these estimates and on local scale
effects of vegetation designs. These local scale effects include
potentially increasing local concentrations due to limiting pollution
dispersion or reducing concentrations through enhanced deposi-
tion and reducing the production of particulate matter.Urban forest
designs that consider sourceesink relationships of PM
2.5
and other
pollutants can be developed to reduce PM
2.5
concentrations and
minimize human exposure to PM
2.5
in cities across the globe.
Acknowledgments
Funding for this project was provided, in part, by the National
Science Foundation (NSF grants DEB-0423476 and BCS-0948952)
through the Baltimore Ecosystem Study-Long Term Ecological
Research (BES-LTER) and the Syracuse Urban Long-term Research
Area Exploratory Award (ULTRA-Ex). The authors thank Jin Huang,
Ryan Stapler and Alex Foreste for their comments on a draft
manuscript.
References
Abbey, D.E., Ostro, B.E., Petersen, F., Burchette, R.J., 1995. Chronic respiratory
symptoms associated with estimated long-term ambient concentrations of fine
particulates less than 2.5 microns in aerodynamic diameter (PM2.5) and other
air pollutants. Journal of Exposure Analysis and Environmental Epidemiology 5
(2), 137e159.
Abt Associates, 2010. BenMAP Environmental Benefits Mapping and Analysis Pro-
gram. Office of Air Quality Planning and Standards U.S. Environmental Protec-
tion Agency, Research Triangle Park, NC, p. 283. http://www.epa.gov/airquality/
benmap/models/BenMAPManualAugust2010.pdf (last accessed 24.05.12.).
Beckett, K.P., Freer, P.H., Taylor, G.,1998. Urbanwoodlands: their role in reducing the
effects of particulate pollution. Environmental Pollution 99, 347e360.
Beckett, K.P., Freer, P.H., Taylor, G., 2000a. Effective tree species for local air-quality
management. Journal of Arboriculture 26 (1), 12e19.
Beckett, K.P., Freer, P.H., Taylor, G., 2000b. Particulate pollution capture by urban
trees: effect of species and windspeed. Global Change Biology 6, 995e1003.
Bell, M.L., Ebisu, K., Peng, R.D., Walker, J., Samet, J.M., Zeger, S.L., Dominici, F., 2008.
Seasonal and regional short-term effects of fine particles on hospital admissions
in 202 U.S. Counties, 1999e2005. American Journal of Epidemiology 168 (11),
1301 e1310.
Cavanagh, J.E., Zawar-Reza, P., Wilson, J.G., 2009. Spatial attenuation of ambient
particulate matter air pollution within an urbanised native forest patch. Urban
Forestry and Urban Greening 8, 21e30.
Chameides, W.L., Lindsay, R.W., Richardson, J., Kiang, C.S., 1988. The role of biogenic
hydrocarbons in urban photochemical smog: Atlanta as a case study. Science
241, 1473e1475.
Colbeck, I., Harrison, R.M., 1985. Dry deposition of ozone: some measurements of
deposition velocity and of vertical profiles to 100 m. Atmospheric Environment
19 (11), 1807e1818 .
Dasch, J.M., 1987. Measurement of dry deposition to surfaces in deciduous and pine
canopies. Environmental Pollution 44, 261e277.
Davidson, K., Hallberg, A., McCubbin, D., Hubbell, B., 2007. Analysis of PM
2.5
using
the environmental benefits mapping and analysis program (BenMAP). Journal
of Toxicology and Environmental Health, Part A 70, 332e346.
Dockery, D.W., Cunningham, J., Damokosh, A.I., Neas, L.M., Spengler, J.D.,
Koutrakis, P., Ware, J.H., Raizenne, M., Speizer, F.E., 1996. Health effects of acid
aerosols on North American children erespiratory symptoms. Environmental
Health Perspectives 104 (5), 500e505.
Fann, N., Risley, D., 2011. The public health context for PM
2.5
and ozone air quality
trends. Air Quality, Atmosphere and Health. http://dx.doi.org/10.1007/s11869-
010-0125-0.
Freer-Smith, P.H., Beckett, K.P., Taylor, G., 2005. Deposition velocities to Sorbus Aria,
Acer campestre, Populus deltoides x trichocarpa ‘Beaupre’, Pinus nigra, and x
Cupressocyparis leylandii for coarse, fine and ultra-fine particles in the urban
environment. Environmental Pollution 133, 157e167.
Freer-Smith, P.H., El-Khatib, A.A., Taylor, G., 2004. Capture of particulate pollution by
trees: a comparison of species typical of semi-arid areas (Ficus nitida and
Eucalyptus globulus) with European and North American species. Water, Air,
and Soil Pollution 155, 173e18 7.
Gallagher, M.W., Beswick, K.M., Duyzer, J., Westrate, H., Choularton, T.W.,
Hummelshoj, P., 1997. Measurement of aerosol fluxes to speulder forest using a
micrometeorological technique. Atmospheric Environment 31 (3), 359e373.
Gromke, C., Ruck, B., 2009. On the impact of trees on dispersion processes of traffic
emissions in street canyons. Boundary-Layer Meteorology 131 (1), 19e34.
Heisler, G.M., 1986. Energy savings with trees. Journal of Arboriculture 12 (5), 113e
125.
Hicks, B.B., Matt, D.R., McMillen, R.T., 1989. A micrometeorological investigation of
the surface exchange of O
3
,SO
2
, and NO
2
: a case study. Boundary-Layer
Meteorology 47, 321e336.
Hodan, W.B., Barnard, W.R., 2004. Evaluating the Contribution of PM
2.5
Precursor
Gases and Re-entrained Road Emissions to Mobile Source PM
2.5
Particulate
Matter Emissions. MACTEC Under Contract to the Federal Highway Adminis-
tration, p. 53. http://www.epa.gov/ttnchie1/conference/ei13/mobile/hodan.pdf
(last accessed 19.06.12.).
Joureava, V.A., Johnson, D.L., Hassett, J.P., Nowak, D.J., Shipunova, N.A., 2002. Dif-
ferences in accumulation of PAHs and metals on the leaves of Tilia x euchlora
and Pyrus calleryana. Environmental Pollution 120, 331e338.
Laden, F., Schwartz, J., Speizer, F.E., Dockery, D.W., 2006. Reduction in fine partic-
ulate air pollution and mortality: extended follow-up of the Harvard six
cities study. American Journal of Respiratory and Critical Care Medicine 173 (6),
667e672.
Lovett, G.M., 1994. Atmospheric deposition of nutrients and pollutants in North
America: an ecological perspective. Ecological Applications 4, 629e650.
Mar, T.F., Koenig, J.Q., Primomo, J., 2010. Associations between asthma emergency
visits and particulate matter sources, including diesel emissions from sta-
tionary generators in Tacoma, Washington. Inhalation Toxicology 22 (6),
445e448.
Mar, T.F., Larson, T.V., Stier, R.A., Claiborn, C., Koenig, J.Q., 2004. An analysis of the
association between respiratory symptoms in subjects with asthma and daily
air pollution in Spokane, Washington. Inhalation Toxicology 16 (13), 809e815.
McDonald, A.G., Bealey, W.J., Fowler, D., Dragosits, U., Skiba, U., Smith, R.I.,
Donovan, R.G., Brett, H.E., Hewitt, C.N., Nemitz, E., 2007. Quantifying the effect
of urban tree planting on concentrations and depositions of PM
10
in two UK
conurbations. Atmospheric Environment 41, 8455e8467.
Moolgavkar, S.H., 2000. Air pollution and hospital admissions for diseases of the
circulatory system in three U.S. metropolitan areas. Journal of the Air and Waste
Management Association 50 (7), 1199e120 6.
Moolgavkar, S.H., May 2003. Air pollution and daily deaths and hospital admissions
in Los Angeles and Cook Counties. In: Revised Analyses of Time-Series Studies
of Air Pollution and Health. Health Effects Institute, Boston, MA.
Murray, F.J., Marsh, L., Bradford, P.A., 1994. Issue Reports. New York State Energy
Plan, vol. II. New York State Energy Office, Albany, NY.
Norris, G., Young Pong, S.N., Koenig, J.Q., Larson, T.V., Sheppard, L., Stout, J.W., 1999.
An association between fine particles and asthma emergency department visits
for children in Seattle. Environmental Health Perspectives 107 (6), 489e493.
Nowak, D.J., Civerolo, K.L., Rao, S.T., Sistla, G., Luley, C.J., Crane, D.E., 2000.
A modeling study of the impact of urban trees on ozone. Atmospheric Envi-
ronment 34, 1610e1613.
Nowak, D.J., Crane, D.E., Stevens, J.C., 2006a. Air pollution removal by urban trees
and shrubs in the United States. Urban Forestry and Urban Greening 4, 115e123.
Nowak, D.J., Hoehn, R.E., Crane, D.E., Stevens, J.C., LeBlanc, C., 2010. Assessing Urban
Forest Effects and Values: Chicago’s Urban Forest. Northern Resource Bulletin
NRS-37. USDA Forest Service, Newtown Square, PA, p. 27.
Nowak, D.J., Hoehn, R.E., Crane, D.E., Stevens, J.C., Walton, J.T., 2006b. Assessing
Urban Forest Effects and Values: Minneapolis’Urban Forest. Northeastern
Resource Bulletin, NE-166. USDA Forest Service, p. 20.
Nowak, D.J., Hoehn, R.E., Crane, D.E., Stevens, J.C., Walton, J.T., 2007a. Assessing
Urban Forest Effects and Values: New York City’s Urban Forest. Northern
Resource Bulletin NRS-9. USDA Forest Service, Newtown Square, PA, p. 24.
Nowak, D.J., Hoehn, R.E., Crane, D.E., Stevens, J.C., Walton, J.T., 2007b. Assessing
Urban Forest Effects and Values: Philadelphia’s Urban Forest. Northern
Resource Bulletin NRS-7. USDA Forest Service, Newtown Square, PA, p. 24.
Nowak, D.J., Hoehn, R.E., Crane, D.E., Stevens, J.C., Walton, J.T., 2007c. Assessing
Urban Forest Effects and Values: San Francisco’s Urban Forest. Northern
Resource Bulletin NRS-8. USDA Forest Service, Newtown Square, PA, p. 24.
Nowak, D.J., Hoehn, R.E., Crane, D.E., Stevens, J.C., Walton, J.T., Bond, J., 2008.
A ground-based method of assessing urban forest structure and ecosystem
services. Arboriculture and Urban Forestry 34 (6), 347e358.
Nowak, D.J., Hoehn, R., Crane, D.E., Weller, L., Davila, A., 2011. Assessing Urban Forest
Effects and Values: Los Angeles’s Urban Forest. Northern Resource Bulletin NRS-
47. USDA Forest Service, Newtown Square, PA, p. 30.
Nowak, D.J., Greenfield, E.J., 2012a. Tree and impervious cover in the United States.
Landscape and Urban Planning 107, 21e30.
Nowak, D.J., Greenfield, E.J., 2012b. Tree and impervious cover change in U.S. cities.
Urban Forestry and Urban Greening 11, 21e30.
Ostro, B.D., 1987. Air pollution and morbidity revisited: a specification test. Journal
of Environmental Economics and Management 14, 87e98.
Ostro, B., Lipsett, M., Mann, J., Braxton-Owens, H., White, M., 2001. Air pollution and
exacerbation of asthma in AfricaneAmerican children in Los Angeles. Epide-
miology 12 (2), 200e208.
Ostro, B.D., Rothschild, S., 1989. Air pollution and acute respiratory morbidity ean
observational study of multiple pollutants. Environmental Research 50 (2),
238e247.
Peng, R.D., Bell, M.L., Geyh, A.S., McDermott, A., Zeger, S.L., Samet, J.M., Dominici, F.,
2009. Emergency admissions for cardiovascular and respiratory diseases and
the chemical composition of fine particle air pollution. Environmental Health
Perspectives 117 (6), 957e963.
D.J. Nowak et al. / Environmental Pollution 178 (2013) 395e402 401
Peng, R.D., Chang, H.H., Bell, M.L., McDermott, A., Zeger, S.L., Samet, J.M.,
Dominici, F., 2008. Coarse particulate matter air pollution and hospital admis-
sions for cardiovascular and respiratory diseases among Medicare patients.
Journal of the American Medical Association 299 (18), 2172e2179.
Peters, A., Dockery, D.W., Muller, J.E., Mittleman, M.A., 2001. Increased particulate
air pollution and the triggering of myocardial infarction. Circulation 103 (23),
2810e2815.
Pope, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D., Ito, K., Thurston, G.D.,
2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine
particulate air pollution. Journal of the American Medical Association 287 (9),
113 2e114 1.
Pope, C.A., Burnett, R.T., Thurston, G.D., Thun, M.J., Calle, E.E., Krewski, D.,
Godleski, J.J., 2004. Cardiovascular mortality and long-term exposure to par-
ticulate air pollution. Circulation 109, 71e77.
Pope, C.A., Dockery, D.W., Spengler, J.D., Raizenne, M.E., 1991. Respiratory health and
PM10 pollution ea daily time series analysis. American Review of Respiratory
Disease 144 (3), 668e674.
Pope, C.A., Muhlestein, J.B., May, H.T., Renlund, D.G., Anderson, J.L., Horne, B.D.,
2006. Ischemic heart disease events triggered by short-term exposure to fine
particulate air pollution. Circulation 114 (23), 2443e2448.
Pullman, M., 2009. Conifer PM
2.5
Deposition and Re-suspension in Wind and Rain
Events. Master’s thesis. Cornell University, p. 51.
Schwartz, J., Neas, L.M., 20 00. Fine particles are more strongly associated than
coarse particles with acute respiratory health effects in schoolchildren. Epide-
miology 11 (1), 6e10.
Slaughter, J.C., Kim, E., Sheppard, L., Sullivan, J.H., Larson, T.V., Claiborn, C., 2005.
Association between particulate matter and emergency room visits, hospital
admissions and mortality in Spokane, Washington. Journal of Exposure Analysis
and Environmental Epidemiology 15 (2), 153e159.
Smith, W.H., 1990. Air Pollution and Forests. Springer, New York.
Sullivan, J., Sheppard, L., Schreuder, A., Ishikawa, N., Siscovick, D., Kaufman, J., 2005.
Relation between short-term fine-particulate matter exposure and onset of
myocardial infarction. Epidemiology 16 (1), 41e48.
Tallis, M., Taylor, G., Sinnett, D., Freer-Smith, P., 2011. Estimating the removal of
atmospheric particulate pollution by the urban tree canopy of London, under
current and future environments. Landscape and Urban Planning 103,129e138.
Tiwary, A., Sinnett, D., Peachey, C., Chalabi, Z., Vardoulakis, S., Fletcher, T.,
Leonardi, G., Grundy, C., Azapagic, A., Hutchings, T.R., 2009. An integrated tool
to assess the role of new planting in PM
10
capture and the human health
benefits: a case study in London. Environmental Pollution 157, 2645e2653.
U.S. Environmental Protection Agency, 1981. User’s Manual for Mixing Height Com-
puter Program.U.S. Environmental Protection Agency,Research Triangle Park, NC.
U.S. Environmental Protection Agency, 1995. PCRAMMIT User’s Guide. U.S. Envi-
ronmental Protection Agency, Research Triangle Park, NC.
U.S. Environmental Protection Agency, 2012a. Environmental Benefits Mapping and
Analysis Program (BenMAP). http://www.epa.gov/air/benmap/ (last accessed
24.05.12.).
U.S. Environmental Protection Agency, 2012b. PM
2.5
and ozone impacts per unit
change in air quality. http://www.epa.gov/airquality/benmap/reduced.html
(last accessed 24.05.12.).
Vos, P.E.J., Maiheu, B., Vankerkom, J., Janssen, S., 2012. Improving local air quality in
cities: to tree or not to tree? Environmental Pollution. in press.
Wang, J., Endreny, T.A., Nowak, D.J., 2008. Mechanistic simulation of urban tree
effects in an urban water balance model. Journal of American Water Resource
Association 44 (1), 75e85.
Woodruff, T.J., Grillo, J., Schoendorf, K.C., 1997. The relationship between selected
causes of postneonatal infant mortality and particulate air pollution in the
United States. Environmental Health Perspectives 105 (6), 608e612.
Woodruff, T.J., Parker, J.D., Schoendorf, K.C., 2006. Fine particulate matter (PM2.5)
air pollution and selected causes of postneonatal infant mortality in California.
Environmental Health Perspectives 114, 786e790.
Zanobetti, A., Franklin, M., Koutrakis, P., Schwartz, J., 2009. Fine particulate air
pollution and its components in association with cause-specific emergency
admissions. Environmental Health 8, 58e60.
Zanobetti, A., Schwartz, J., 20 06. Air pollution and emergency admissions in Boston,
MA. Journal of Epidemiology and Community Health 60 (10), 890e895.
D.J. Nowak et al. / Environmental Pollution 178 (2013) 395e402402
